Adaptive Multiple Importance Sampling for Gaussian Processes

In applications of Gaussian processes where quantification of uncertainty is a strict requirement, it is necessary to accurately characterize the posterior distribution over Gaussian process covariance parameters. Normally, this is done by means of standard Markov chain Monte Carlo (MCMC) algorithms. Motivated by the issues related to the complexity of calculating the marginal likelihood that can make MCMC algorithms inefficient, this paper develops an alternative inference framework based on Adaptive Multiple Importance Sampling (AMIS). This paper studies the application of AMIS in the case of a Gaussian likelihood, and proposes the Pseudo-Marginal AMIS for non-Gaussian likelihoods, where the marginal likelihood is unbiasedly estimated. The results suggest that the proposed framework outperforms MCMC-based inference of covariance parameters in a wide range of scenarios and remains competitive for moderately large dimensional parameter spaces.Comment: 27 page

Versatile Propylene-Based Polyolefins with Tunable Molecular Structure through Tailor-Made Catalysts and Polymerization Process

Since the discovery of Ziegler-Natta catalysts for olefin polymerization in the 1950s, the production of polyolefins with a variety of properties has continuously grown with rapid development of catalyst technology combined with polymerization process innovation. For propylene-based polyolefin, various polyolefins with distinctive characteristic of mechanical and optical properties were made with specific catalysts in commercial industries owned especially by those large worldwide companies. In this chapter, Ziegler-Natta catalysts, metallocene catalysts, and post-metallocene catalysts for PP polymerization are discussed in detail. Gas phase, bulk, slurry, and solution polymerization processes, such as Spheripol (Basell), Hypol (Mitsui Chemicals), Unipol (Dow Chemical), Innovene (INEOS), Novelen (BASF), Spherizone (Basell), and Borstar (Borealis), developed by the industrial tycoons were reviewed. The molecular architecture of the PP-based polyolefins could be tailored precisely using specific high-performance catalyst in an appropriate polymerization process, and different types of PPs, including homopolypropylene (HPP), random copolypropylene (RPP), impact PP, PP-based block copolymer, functionalized PP, etc., are produced. The relationship between molecular structure and performance of the PP-based polyolefins is also discussed thereof

Adaptive multiple importance sampling for Gaussian processes and its application in social signal processing

Social signal processing aims to automatically understand and interpret social signals (e.g. facial expressions and prosody) generated during human-human and human-machine interactions. Automatic interpretation of social signals involves two fundamentally important aspects: feature extraction and machine learning. So far, machine learning approaches applied to social signal processing have mainly focused on parametric approaches (e.g. linear regression) or non-parametric models such as support vector machine (SVM). However, these approaches fall short of taking into account any uncertainty as a result of model misspecification or lack interpretability for analyses of scenarios in social signal processing. Consequently, they are less able to understand and interpret human behaviours effectively. Gaussian processes (GPs), that have gained popularity in data analysis, offer a solution to these limitations through their attractive properties: being non-parametric enables them to flexibly model data and being probabilistic makes them capable of quantifying uncertainty. In addition, a proper parametrisation in the covariance function makes it possible to gain insights into the application under study. However, these appealing properties of GP models hinge on an accurate characterisation of the posterior distribution with respect to the covariance parameters. This is normally done by means of standard MCMC algorithms, which require repeated expensive calculations involving the marginal likelihood. Motivated by the desire to avoid the inefficiencies of MCMC algorithms rejecting a considerable number of expensive proposals, this thesis has developed an alternative inference framework based on adaptive multiple importance sampling (AMIS). In particular, this thesis studies the application of AMIS for Gaussian processes in the case of a Gaussian likelihood, and proposes a novel pseudo-marginal-based AMIS (PM-AMIS) algorithm for non-Gaussian likelihoods, where the marginal likelihood is unbiasedly estimated. Experiments on benchmark data sets show that the proposed framework outperforms the MCMC-based inference of GP covariance parameters in a wide range of scenarios. The PM-AMIS classifier - based on Gaussian processes with a newly designed group-automatic relevance determination (G-ARD) kernel - has been applied to predict whether a Flickr user is perceived to be above the median or not with respect to each of the Big-Five personality traits. The results show that, apart from the high prediction accuracies achieved (up to 79% depending on the trait), the parameters of the G-ARD kernel allow the identification of the groups of features that better account for the classification outcome and provide indications about cultural effects through their weight differences. Therefore, this demonstrates the value of the proposed non-parametric probabilistic framework for social signal processing. Feature extraction in signal processing is dominated by various methods based on short time Fourier transform (STFT). Recently, Hilbert spectral analysis (HSA), a new representation of signal which is fundamentally different from STFT has been proposed. This thesis is also the first attempt to investigate the extraction of features from this newly proposed HSA and its application in social signal processing. The experimental results reveal that, using features extracted from the Hilbert spectrum of voice data of female speakers, the prediction accuracy can be achieved by up to 81% when predicting their Big-Five personality traits, and hence show that HSA can work as an effective alternative to STFT for feature extraction in social signal processing

Performance-based plastic design method of high-rise steel frames

Under major earthquakes, high-rise steel moment frames designed according to the current codes will experience an inelastic deformation, which is difficult to predict and control. According to the principle of work-energy balance, a performance-based plastic design (PBPD) methodology is put forward for the design of high-rise steel frames in this study. In this method, the target drift and yield mechanisms are pre-selected as key performance criteria. The design base shear in a given earthquake level is calculated based on the work-energy balance principle that the work required to push the structure monotonically to the target drift is equal to the energy needed by an equivalent single degree of freedom to reach the same state. The plastic design is utilized to design the frame components and connections so as to attain the desired yield mechanism and behavior. The method has been adopted to design a ten-story steel moment resisting frame, and has been validated by nonlinear dynamic time history analyses and pushover analysis. The results indicate that the frames develop targeted strong column sway mechanisms, and the story drifts are less than the target values, thus satisfying the anticipated performance objectives. The addressed method herein can form a basis for the performance-based plastic design of high-rise steel moment resisting frames

The Application of Exemplarist Moral Theory and Problem-Based Learning in the Course of Structural Mechanics

Structural mechanics is an important basic course for undergraduates majoring in civil engineering. However, due to the difficulty and extent of the content, students are often not able to master the course. Problem-based learning is an excellent way of teaching engineering, and character education can improve students’ performance. This study explores the combined application of problem-based learning and exemplarist moral theory. In this model, students analyze the structure and force of buildings, and they learn about the historical stories behind them. In this way, students improve their morality, civility, performance, and intellect. In character education, the use of case studies and examples can increase students’ interest in the course, improve classroom participation, enrich teaching connotations, and strengthen students’ understanding of basic concepts and their ability to memorize them

Numerical Study of the Wake Flow of a Wind Turbine with Consideration of the Inflow Turbulence

Considering the fact that wind turbines operate at the bottom of the atmospheric boundary layer (ABL) where the turbulence is at a high level, and the difficulty of mesh generation in the fully modeled numerical simulation, it is necessary to carry out researches to study the wake flow of wind turbines with consideration of the inflow turbulence. Therefore, a numerical method generating turbulence was proposed and the results show good agreement with those in experiments, based on which the flow fields in the wake of a wind turbine at two tip speed ratios are examined in detail through three actuator methods, namely, ADM, ADM-R and ALM. The performances of these methods were studied and the error sources for each method are clarified. Moreover, the computational efficiency were revealed and the influencing factor for the efficiency is concluded. Besides, the equilibrium relation of the N-S equation in the wake is revealed, which provides a theoretical basis for the optimal arrangement of the wind turbine. It shows that the mean velocity and fluctuating velocity vary greatly near the wind turbine, and become stable gradually away from the wind turbine. The results of ALM method shows the best agreement with the experiment. At near wake region, the turbulent stress term, pressure gradient term and convection term mainly contribute to the equation equilibrium, and convection term is in equilibrium with the turbulent stress term at the far wake

Sustainable development for international Chinese language education along the belt and road countries in the post-epidemic era: a SWOT-AHP approach

The purpose of this study is to provide sustainable growth strategies and practical methods to realize the sustainable development of International Chinese language education along the Belt and Road Countries in the Post-Epidemic era. This article consists of a SWOT (Strength, Weakness, Opportunity, Threat) analysis of developing International Chinese language education in Belt and Road Countries. and in-depth interviews with experts in International Chinese language education in China. The Analytic Hierarchy Process (AHP) is adopted to the finalized and quantified SWOT matrix to incorporate experts’ ideas.The result showed that the total strength and total opportunity of International Chinese language education in Belt and Road Countries are much greater than that of total weakness and total threat, which indicates that the opportunities outweigh threats, and advantage outweigh disadvantage. Therefore, the growth-oriented strategy shall be applied to spur the sustainable development of Belt and Road Countries’ International Chinese language education. In addition, the setbacks should also be taken into serious consideration in order to overcome the weakness and the threat

Spin photonics on chip based on a twinning crystal metamaterial

Two-dimensional photonic circuits with high capacity are essential for a wide range of applications in next-generation photonic information technology and optoelectronics. Here we demonstrate a multi-channel spin-dependent photonic device based on a twinning crystal metamaterial. The structural symmetry and material symmetry of the twinning crystal metamaterial enable a total of 4 channels carrying different transverse spins because of the spin-momentum locking. The orientation of the anisotropy controls the propagation direction of each signal, and the rotation of the E-field with respect to energy flow determines the spin characteristics during input/output coupling. Leveraging this mechanism, the spin of an incident beam can be maintained during propagation on-chip and then delivered back into the free space, offering a new scheme for metamaterial-based spin-controlled nano-photonic applications